Characterization of bowing strokes in violin playing in terms of controls and sound: Differences between bouncing and on-string bow strokes
Bibliographic record
Abstract
Bowing is the main element in sound production during a violin performance and one of the most basic and important expressive resources for the musician. In the lowest level, control parameters such as force, velocity, or bow-bridge distance are directly determining the characteristics of the sound. In a higher level, bowing strokes constitute one of the main mechanisms for structuring the performance. There are many different kinds of bowing strokes, and they are commonly classified into on-string, if the attack happens with the bow on the string and off-string, if the bow is bouncing. From a database of violin performances containing multimodal data including sound and gestures, a set of spectral features and instrumental controls is extracted and the database is segmented into intra-note segments (attack, sustain, and release). A characterization of bowing strokes and a comparison between bouncing and on-string strokes in terms of bowing controls and sound at the intra-note segments is presented.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Acoustics study characterizing violin bowing strokes; the object is sound production.
It analyzes violin performance and sound production, not research practice.
Characterizes violin bowing controls and sound; music performance science, not research practice.
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".